extracting/copy ranges of columns from large data file

This looks like a case where cut will do better than awk:

cut -d , -f1-20 Precipitation.csv > aaa1
cut -d , -f21-40 Precipitation.csv > aaa2
cut -d , -f41-100 Precipitation.csv > aaa3

-d , specifies the delimiter (a comma, since the input is called CSV, but you can change that). -f N-M picks out fields N through M inclusive to be in the output. If there are embedded commas within any field it will break - in that case you probably want a real CSV parser, although it is possible to hack something together if that really isn't possible.

The output will use the same delimiter as the input. GNU cut supports a --output-delimiter=STRING option to set a different delimiter, but I don't think any others do. FreeBSD's cut includes a -w split-by-whitespace option that is closer to what awk does by default. If you want one of those behaviours elsewhere, transforming the delimiter before/after is probably your best bet.

A follow-up on Michael Homer's answer:

To avoid reading the file several times with shells supporting process substitution (ksh, zsh, bash):

 tee < Precipitation.csv >(cut -d, -f1-20 > aaa1) >(cut -d, -f21-40 > aaa2) | 
   cut -d, -f41- > aaa3

With perl:

perl -F',' -anle 'print join ",", @F[0..19]' Precipitation.csv > aaa1
perl -F',' -anle 'print join ",", @F[20..39]' Precipitation.csv > aaa2
perl -F',' -anle 'print join ",", @F[40..99]' Precipitation.csv > aaa3

As I understand your problem, this should be all you need - and it's all processed in a single stream, so you should handle the big file much more efficiently that way.

I did a small test on this like:

{   sed -e '/\n\n/{s///w aaa3
    d' -e '};s/,/\n/20;P;G;D' |
    sed -ne 'w aaa1' -e 'n;p'
} <<DATA > aaa2
$(
    seq -s, 100 | tee - - - -
)
DATA

Then I printed the results like:

for f in 1 2 3
do  printf '\n%s\n' aaa$f
    cat aaa$f
done

And success!

aaa1
1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20
1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20
1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20
1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20
1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20

aaa2
21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40
21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40
21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40
21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40
21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40

aaa3
41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100
41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100
41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100
41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100
41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100

This provides better performance as we are reading the input file only once

awk '{out1=$1; for(i=2; i<=20; i++) {out1 = (out1 FS $i)}
  out2=$21; for(i=22; i<=40; i++) {out2=(out2 FS $i)}
  out3=$41; for(i=42; i<=100; i++) {out3=(out3 FS $i)}
  print out1 > "aaaaa1"; print out2 > "aaaaa2"; print out3 > "aaaaa3"}' Precipitation.csv

or you can type all the field numbers in the below command. Replace '...' with all the required field numbers

awk '{print $1,$2,$3,...$19,$20 > "aaaaa1";
  print $21,$22,$23,...$39,$40 > "aaaaa2";
  print $41,$42,$43,...$99,$100 > "aaaaa3"}' Precipitation.csv